Efficient Vertical Structure Correlation and Power Line Inference
High-resolution three-dimensional data from sensors such as LiDAR are sufficient to find power line towers and poles but do not reliably map relatively thin power lines. In addition, repeated detections of the same object can lead to confusion while data gaps ignore known obstacles. The slow or fail...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/5/1686 |
| _version_ | 1797263838501928960 |
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| author | Paul Flanigen Ella Atkins Nadine Sarter |
| author_facet | Paul Flanigen Ella Atkins Nadine Sarter |
| author_sort | Paul Flanigen |
| collection | DOAJ |
| description | High-resolution three-dimensional data from sensors such as LiDAR are sufficient to find power line towers and poles but do not reliably map relatively thin power lines. In addition, repeated detections of the same object can lead to confusion while data gaps ignore known obstacles. The slow or failed detection of low-salience vertical obstacles and associated wires is one of today’s leading causes of fatal helicopter accidents. This article presents a method to efficiently correlate vertical structure observations with existing databases and infer the presence of power lines. The method uses a spatial hash key which compares an observed tower location to potential existing tower locations using nested hash tables. When an observed tower is in the vicinity of an existing entry, the method correlates or distinguishes objects based on height and position. When applied to Delaware’s Digital Obstacle File, the average horizontal uncertainty decreased from 206 to 56 ft. The power line presence is inferred by automatically comparing the proportional spacing, height, and angle of tower sets based on the more accurate database. Over 87% of electrical transmission towers were correctly identified with no false negatives. |
| first_indexed | 2024-04-25T00:19:22Z |
| format | Article |
| id | doaj.art-c42d206c1ed848c4860d90e24b9e384e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:19:22Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c42d206c1ed848c4860d90e24b9e384e2024-03-12T16:55:38ZengMDPI AGSensors1424-82202024-03-01245168610.3390/s24051686Efficient Vertical Structure Correlation and Power Line InferencePaul Flanigen0Ella Atkins1Nadine Sarter2Robotics Department, University of Michigan, Ann Arbor, MI 48109, USAAerospace and Ocean Engineering Department, Virginia Tech, Blacksburg, VA 24061, USARobotics Department, University of Michigan, Ann Arbor, MI 48109, USAHigh-resolution three-dimensional data from sensors such as LiDAR are sufficient to find power line towers and poles but do not reliably map relatively thin power lines. In addition, repeated detections of the same object can lead to confusion while data gaps ignore known obstacles. The slow or failed detection of low-salience vertical obstacles and associated wires is one of today’s leading causes of fatal helicopter accidents. This article presents a method to efficiently correlate vertical structure observations with existing databases and infer the presence of power lines. The method uses a spatial hash key which compares an observed tower location to potential existing tower locations using nested hash tables. When an observed tower is in the vicinity of an existing entry, the method correlates or distinguishes objects based on height and position. When applied to Delaware’s Digital Obstacle File, the average horizontal uncertainty decreased from 206 to 56 ft. The power line presence is inferred by automatically comparing the proportional spacing, height, and angle of tower sets based on the more accurate database. Over 87% of electrical transmission towers were correctly identified with no false negatives.https://www.mdpi.com/1424-8220/24/5/1686databaseflight hazardslow-altitude flighthelicopter operationsadvanced aerial mobility |
| spellingShingle | Paul Flanigen Ella Atkins Nadine Sarter Efficient Vertical Structure Correlation and Power Line Inference Sensors database flight hazards low-altitude flight helicopter operations advanced aerial mobility |
| title | Efficient Vertical Structure Correlation and Power Line Inference |
| title_full | Efficient Vertical Structure Correlation and Power Line Inference |
| title_fullStr | Efficient Vertical Structure Correlation and Power Line Inference |
| title_full_unstemmed | Efficient Vertical Structure Correlation and Power Line Inference |
| title_short | Efficient Vertical Structure Correlation and Power Line Inference |
| title_sort | efficient vertical structure correlation and power line inference |
| topic | database flight hazards low-altitude flight helicopter operations advanced aerial mobility |
| url | https://www.mdpi.com/1424-8220/24/5/1686 |
| work_keys_str_mv | AT paulflanigen efficientverticalstructurecorrelationandpowerlineinference AT ellaatkins efficientverticalstructurecorrelationandpowerlineinference AT nadinesarter efficientverticalstructurecorrelationandpowerlineinference |